{\it SimRank} is a classic measure of the similarities of nodes in a graph.
Given a node u in graph G=(V,E), a {\em single-source SimRank query}
returns the SimRank similarities s(u,v) between node u and each node v∈V. This type of queries has numerous applications in web search and social
networks analysis, such as link prediction, web mining, and spam detection.
Existing methods for single-source SimRank queries, however, incur query cost
at least linear to the number of nodes n, which renders them inapplicable for
real-time and interactive analysis.
{ This paper proposes \prsim, an algorithm that exploits the structure of
graphs to efficiently answer single-source SimRank queries. \prsim uses an
index of size O(m), where m is the number of edges in the graph, and
guarantees a query time that depends on the {\em reverse PageRank} distribution
of the input graph. In particular, we prove that \prsim runs in sub-linear time
if the degree distribution of the input graph follows the power-law
distribution, a property possessed by many real-world graphs. Based on the
theoretical analysis, we show that the empirical query time of all existing
SimRank algorithms also depends on the reverse PageRank distribution of the
graph.} Finally, we present the first experimental study that evaluates the
absolute errors of various SimRank algorithms on large graphs, and we show that
\prsim outperforms the state of the art in terms of query time, accuracy, index
size, and scalability.Comment: ACM SIGMOD 201